Campus 2026: Data Science Engineer

DraftKings Inc.•Boston, MA

About The Position

At DraftKings, AI is becoming an integral part of both our present and future, powering how work gets done today, guiding smarter decisions, and sparking bold ideas. It’s transforming how we enhance customer experiences, streamline operations, and unlock new possibilities. Our teams are energized by innovation and readily embrace emerging technology. We’re not waiting for the future to arrive. We’re shaping it, one bold step at a time. To those who see AI as a driver of progress, come build the future together. The Crown Is Yours As a Data Science Engineer, you'll join a high-impact team tackling complex analytical challenges across sports and entertainment. You'll work alongside Machine Learning engineers and cross-functional partners to turn large, complex datasets into scalable, production-ready solutions that power our platforms. From building models that enable new product features to uncovering insights that influence strategy, you'll help shape how data drives innovation across the organization. In this role, you'll strengthen your technical foundation while contributing to systems that operate at scale and directly impact our customers.

Requirements

Bachelor's degree in Data Science, Computer Science, Engineering, Mathematics, or a related field.
At least 1 year of professional experience in Data Science, Machine Learning, or a related technical role.
Proficiency in Python, including experience with libraries such as pandas, NumPy, and PySpark.
A solid foundation in statistics, probability, and applied analytical methods.
Experience working with complex datasets and integrating data from multiple sources.
Ability to translate business problems into clear technical approaches and communicate results effectively to cross-functional partners.

Responsibilities

Analyze large, complex datasets to uncover insights and deliver actionable recommendations through exploratory data analysis.
Design, develop, and evaluate quantitative models using machine learning and advanced analytics techniques.
Build and optimize scalable data pipelines and algorithmic engines that power product features and internal tools.
Partner with Machine Learning engineers and cross-functional stakeholders to translate business needs into data-driven solutions.
Contribute to model deployment, performance monitoring, and continuous improvement in production environments.